CNN-based facial landmark localisation using Wing Loss

This software is developed by Zhenhua Feng from the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey. The software is implemented by Matlab and powered by the MatConvNet toolbox.

If you use this software, please cite the following publication:

Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu. Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks, IEEE Conference on Computer Vision and Patten Recognition (CVPR), Salt Lake City, USA, 2018.

@inproceedings{feng2018wing,
title={Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks},
author={Feng, Zhen-Hua and Kittler, Josef and Awais, Muhammad and Huber, Patrik and Wu, Xiao-Jun},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on},
year={2018},
pages ={2235-2245},
organization={IEEE}
}

You can download the paper from HERE.

News

2018-06-16: Add the demo code as well as two pretrained CNN6 models on the AFLW dataset with 19 facial landmarks
2018-03-29: The pre-trained model and test code are coming soon.

New Results on the COFW and WFLW datasets

Results on COFW

Method	NME(%)	Failure Rate(%)
CNN6 (Wing+PDB)	5.44	3.75
ResNet50 (Wing+PDB)	5.07	3.16

Results on WFLW

Metric	Method	FullSet	Pose	Expression	Illumination	Makeup	Occlusion	Blur
NME(%)	ESR	11.13	25.88	11.47	10.49	11.05	13.75	12.20
	SDM	10.29	24.10	11.45	9.32	9.38	13.03	11.28
	CFSS	9.07	21.36	10.09	8.30	8.74	11.76	9.96
	DVLN	6.08	11.54	6.78	5.73	5.98	7.33	6.88
	LAB	5.27	10.24	5.51	5.23	5.15	6.79	6.32
	ResNet50 (Wing+PDB)	4.99	8.43	5.21	4.88	5.26	6.21	5.81
Failure Rate (%)	ESR	35.24	90.18	42.04	30.80	38.84	47.28	41.40
	SDM	29.40	84.36	33.44	26.22	27.67	41.85	35.32
	CFSS	20.56	66.26	23.25	17.34	21.84	32.88	23.67
	DVLN	10.84	46.93	11.15	7.31	11.65	16.30	13.71
	LAB	7.56	28.83	6.37	6.73	7.77	13.72	10.74
	ResNet50 (Wing+PDB)	5.64	23.31	4.14	4.87	8.74	11.69	7.50
AUC@0.1	ESR	0.2774	0.0177	0.1981	0.2953	0.2485	0.1946	0.2204
	SDM	0.3002	0.0226	0.2293	0.3237	0.3125	0.2060	0.2398
	CFSS	0.3659	0.0632	0.3157	0.3854	0.3691	0.2688	0.3037
	DVLN	0.4551	0.1474	0.3889	0.4743	0.4494	0.3794	0.3973
	LAB	0.5323	0.2345	0.4951	0.5433	0.5394	0.4490	0.4630
	ResNet50 (Wing+PDB)	0.5585	0.3309	0.4979	0.5631	0.5460	0.4985	0.5010

Pre-trained models

Uploaded

cnn6_v0_aflw: pre-trained CNN-6 model on the AFLW-FULL dataset, using the CNN6 architecture as shown in the paper
cnn6_v1_aflw: similar to cnn6_v0 but with doubled filter sizes, which performs better than the original CNN6 but a little bit slower

Installation

Download and install MatConvNet to pathToMatConvNet/.
Modify the path to MatConvNet in demo.m and run the script

License

This soft ware is released under the Apache 2.0 license.

Contact

Dr Zhenhua Feng

Centre for Vision, Speech and Signal Processing

University of Surrey, Guildford GU2 7XH, United Kingdom

z.feng@surrey.ac.uk, fengzhenhua2010@gmail.com

FengZhenhua / Wing-Loss

readme